System and method for high-speed transfer of small data sets

ABSTRACT

A system and method for high-speed transfer of small data sets, that provides near-instantaneous bit-level lossless compression, that is ideal for communications environments that cannot tolerate even small amounts of data corruption, have very low latency tolerance, where data has a low entropy rate, and where every bit costs the user bandwidth, power, or time so that deflation is worthwhile. Where some loss of data can be tolerated, the system and method can be configured for use as lossy compression.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 15/975,741, titled “SYSTEM AND METHOD FOR DATA STORAGE,TRANSFER, SYNCHRONIZATION, AND SECURITY”, filed on May 9, 2018, whichclaims priority to U.S. provisional patent application 62/578,824 titled“MASSIVE DATA STORAGE, TRANSFER, SYNCHRONIZATION, AND SECURITY SYSTEM”,filed on Oct. 30, 2017, the entire specifications of each of which areincorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention is in the field of computer data storage andtransmission.

Discussion of the State of the Art

In order to take advantage of the rapidly-growing and transformativepower of modern computing, the ability to quickly transmit data betweencomputing endpoints is indispensable. Such high-throughput communicationis under constant strain, and the problem will only grow with time astechnology improves. There are many solution strategies used or underdevelopment to enable the passing of ever-greater flow of data overdifferent types of computing interconnects, such as sophisticatedrouting algorithms for network hubs and data buses, exploitation of newphysics to enable deeper multiplexing, highly parallelized hardware andmeticulously engineered standards for wireless data transmission,nanoscale circuit geometry for onboard data flow control, just to name afew.

One particularly important paradigm that is both extremely general andeffective is compression, or “source coding”, dating back in our modernunderstanding to the seminal 1948 work of Clause Shannon. During the1970s, 80s, and 90s, lossless compression algorithms grew increasinglysophisticated, gaining in speed, growing in capacity, and approaching anefficiency close to Shannon's “entropy bound”. This central theorem ofinformation theory states that the compression ratio (also referred toas the “compression power” or “deflation factor”—the ratio of the sizeof the compressed data to the size of the source data) can never exceedon average the encoded random variable's entropy rate (the density,measured in bits, of revealed information). While classical approachessuch as Huffman Coding and Lempel-Ziv-Welch (LZW) are theoreticallyoptimal asymptotically, their various widely-used implementations can bepainfully slow and perform poorly on short data streams in addition tobeing extremely sensitive to noise and nearly impossible to employ forrandom access. This is due to practical constraints of file systems,memory constraints, and the like, but is also a consequence of thehistorical focus of such methods on encoding larger files for storagepurposes. Even the most cutting-edge compression algorithms such asBroth and Zstd designed to handle short files do not provide muchbenefit below a filesize of around 1 kilobyte, or 80000 bits, and areusually used for much larger files.

What is needed is a new solution that provides near-instant bit-levellossless compression, suitable for use on very small amounts of datasuitable for storage or transmission without data loss or corruption.

SUMMARY OF THE INVENTION

The inventor has developed a system and method for high-speed transferof small data sets, that provides near-instantaneous bit-level losslesscompression, that is ideal for communications environments that cannottolerate even small amounts of data corruption, have very low latencytolerance, where data has a low entropy rate, and where every bit coststhe user bandwidth, power, or time so that deflation is worthwhile.

Examples of applications of the invention include vehicular crashsensors, electronic stock trading, instant messaging, explosion andradioisotope detectors, datacenter interconnects, implantable medicaldevices, security motion sensors, astronomical observation devices, andtelephony.

According to a preferred embodiment, a system for high-speed transfer ofsmall data sets is disclosed, comprising: a customized library generatorcomprising at least a plurality of programming instructions stored inthe memory of, and operating on at least one processor of, a computingdevice, wherein the plurality of programming instructions, whenoperating on the at least one processor, cause the computing device to:receive a first dataset comprising a plurality of words, each wordcomprising a string of bits, wherein the first dataset is believed to berepresentative of subsequent datasets; count the plurality of words toproduce an occurrence frequency for each word; create a first Huffmanbinary tree based on the frequency of occurrences of each word in thefirst dataset; assign a Huffman codeword to each observed word in thefirst dataset according to the first Huffman binary tree; construct aword library, wherein the word library stores the codewords and theircorresponding words as key-value pairs in the library of key-valuepairs; create a second Huffman binary tree with a maximum codewordlength shorter than the maximum codeword length in the first Huffmanbinary tree, and containing all combinations of such codewords to thatshorter maximum length; assign a word to each Huffman codeword in thesecond Huffman binary tree; and add each word and its correspondingcodeword, to the word library as key-value pairs in the library ofkey-value pairs; a transmission encoder comprising at least a pluralityof programming instructions stored in the memory of, and operating on atleast one processor of, a computing device, wherein the plurality ofprogramming instructions, when operating on the at least one processor,cause the computing device to: receive one or more subsequent datasets,each comprising a plurality of words, each word comprising a string ofbits; compare each word in the subsequent dataset or datasets againstthe word library; if a word is not a mismatch, append the word'scodeword to a transmission data stream; if a word is a mismatch, appenda mismatch code to the transmission data stream followed by theunencoded word; and transmit or store the transmission data stream; atransmission decoder, comprising at least a plurality of programminginstructions stored in the memory of, and operating on at least oneprocessor of, a computing device, wherein the plurality of programminginstructions, when operating on the at least one processor, cause thecomputing device to: receive one or more datasets, each comprising aplurality of codewords, each codeword comprising a string of bits;compare each codeword in the dataset or datasets against the wordlibrary; if a codeword is not a mismatch, append the codeword's word toa transmission data stream; if a codeword is a mismatch codeword,discard the mismatch code and append the following word to thetransmission data stream; and transmit or store the transmission datastream; a hybrid encoder comprising at least a plurality of programminginstructions stored in the memory of, and operating on at least oneprocessor of, a computing device, wherein the plurality of programminginstructions, when operating on the at least one processor, cause thecomputing device to: receive mismatched words from the transmissionencoder; parse the mismatched words into partial words that correspondto a codeword in the second Huffman tree; and return the codeword foreach partial word to the transmission encoder; and a hybrid decodercomprising at least a plurality of programming instructions stored inthe memory of, and operating on at least one processor of, a computingdevice, wherein the plurality of programming instructions, whenoperating on the at least one processor, cause the computing device to:receive mismatched codewords from the transmission decoder; compare eachcodeword against the word library; and return the word associated withthe mismatched codeword to the transmission encoder.

According to another preferred embodiment, a method for high-speedtransmission of small data sets using a word library is disclosed,comprising the steps of: receiving a first dataset comprising aplurality of words, each word comprising a string of bits, wherein thefirst dataset is believed to be representative of subsequent datasets;counting the plurality of words to produce an occurrence frequency foreach word; creating a first Huffman binary tree based on the frequencyof occurrences of each word in the first dataset; assigning a Huffmancodeword to each observed word in the first dataset according to thefirst Huffman binary tree; constructing a word library, wherein the wordlibrary stores the codewords and their corresponding words as key-valuepairs in the library of key-value pairs; creating a second Huffmanbinary tree with a maximum codeword length shorter than the maximumcodeword length in the first Huffman binary tree, and containing allcombinations of such codewords to that shorter maximum length; assigninga word to each Huffman codeword in the second Huffman binary tree;adding each word and its corresponding codeword, to the word library askey-value pairs in the library of key-value pairs; receiving, at atransmission encoder one or more subsequent datasets, each comprising aplurality of words, each word comprising a string of bits; comparingeach word in the subsequent dataset or datasets against the wordlibrary; if a word is not a mismatch, appending the word's codeword to atransmission data stream; if a word is a mismatch, appending a mismatchcode to the transmission data stream followed by the unencoded word;transmitting or storing the transmission data stream; receiving, at atransmission decoder, one or more datasets, each comprising a pluralityof codewords, each codeword comprising a string of bits; comparing eachcodeword in the dataset or datasets against the word library; if acodeword is not a mismatch, append the codeword's word to a transmissiondata stream; if a codeword is a mismatch codeword, discard the mismatchcode and append the following word to the transmission data stream;transmitting or storing the transmission data stream; receiving, at ahybrid encoder, mismatched words from the transmission encoder; parsingthe mismatched words into partial words that correspond to a codeword inthe second Huffman tree; returning the codeword for each partial word tothe transmission encoder; receiving, at a hybrid decoder, mismatchedcodewords from the transmission decoder; comparing each codeword againstthe word library; and returning the word associated with the mismatchedcodeword to the transmission encoder.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a diagram showing an exemplary system architecture, accordingto a preferred embodiment of the invention.

FIG. 2 is a diagram showing a more detailed architecture for acustomized library generator.

FIG. 3 is a diagram showing a more detailed architecture for a libraryoptimizer.

FIG. 4 is a diagram showing a more detailed architecture for atransmission and storage engine.

FIG. 5 is a method diagram illustrating key system functionalityutilizing an encoder and decoder pair, according to a preferredembodiment.

FIG. 6 is a method diagram illustrating possible use of a hybridencoder/decoder to improve the compression ratio, according to apreferred aspect.

FIG. 7 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 8 is a block diagram illustrating an exemplary logical architecturefor a client device.

FIG. 9 is a block diagram showing an exemplary architectural arrangementof clients, servers, and external services.

FIG. 10 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodfor high-speed transfer of small data sets, that providesnear-instantaneous bit-level lossless compression, that is ideal forcommunications environments that cannot tolerate even small amounts ofdata corruption, have very low latency tolerance, where data has a lowentropy rate, and where every bit costs the user bandwidth, power, ortime so that compression is worthwhile.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

The term “bit” refers to the smallest unit of information that can bestored or transmitted. It is in the form of a binary digit (either 0 or1). In terms of hardware, the bit is represented as an electrical signalthat is either off (representing 0) or on (representing 1).

The term “byte” refers to a series of bits exactly eight bits in length.

The terms “compression” and “deflation” as used herein mean therepresentation of data in a more compact form than the original dataset.Compression and/or deflation may be either “lossless”, in which the datacan be reconstructed in its original form without any loss of theoriginal data, or “lossy” in which the data can be reconstructed in itsoriginal form, but with some loss of the original data.

The terms “compression ratio”, “compression factor”, “deflation ratio”,and deflation factor” as used herein all mean the size of the originaldata relative to the size of the compressed data.

The term “data” means information in any computer-readable form.

A “database” or “data storage subsystem” (these terms may be consideredsubstantially synonymous), as used herein, is a system adapted for thelong-term storage, indexing, and retrieval of data, the retrievaltypically being via some sort of querying interface or language.“Database” may be used to refer to relational database managementsystems known in the art, but should not be considered to be limited tosuch systems. Many alternative database or data storage systemtechnologies have been, and indeed are being, introduced in the art,including but not limited to distributed non-relational data storagesystems such as Hadoop, column-oriented databases, in-memory databases,and the like. While various aspects may preferentially employ one oranother of the various data storage subsystems available in the art (oravailable in the future), the invention should not be construed to be solimited, as any data storage architecture may be used according to theaspects. Similarly, while in some cases one or more particular datastorage needs are described as being satisfied by separate components(for example, an expanded private capital markets database and aconfiguration database), these descriptions refer to functional uses ofdata storage systems and do not refer to their physical architecture.For instance, any group of data storage systems of databases referred toherein may be included together in a single database management systemoperating on a single machine, or they may be included in a singledatabase management system operating on a cluster of machines as isknown in the art. Similarly, any single database (such as an expandedprivate capital markets database) may be implemented on a singlemachine, on a set of machines using clustering technology, on severalmachines connected by one or more messaging systems known in the art, orin a master/slave arrangement common in the art. These examples shouldmake clear that no particular architectural approaches to databasemanagement is preferred according to the invention, and choice of datastorage technology is at the discretion of each implementer, withoutdeparting from the scope of the invention as claimed.

The term “dataset” or “data set” refers to a grouping of data for aparticular purpose. One example of a data set might be a word processingfile containing text and formatting information.

Conceptual Architecture

FIG. 1 is a diagram showing an exemplary system architecture 100,according to a preferred embodiment of the invention. Incoming trainingdata sets may be received at a customized library generator 200 thatprocesses training data to produce a customized word library 101comprising key-value pairs of data words (each comprising a string ofbits) and their corresponding calculated binary Huffman codewords. Theresultant word library 101 may then be processed by a library optimizer300 to reduce size and improve efficiency, for example by pruninglow-occurrence data entries or calculating approximate codewords thatmay be used to match more than one data word. A transmissionencoder/decoder 400 may be used to receive incoming data intended forstorage or transmission, process the data using a word library 101 toretrieve codewords for the words in the incoming data, and then appendthe codewords (rather than the original data) to an outbound datastream. Each of these components is described in greater detail below,illustrating the particulars of their respective processing and otherfunctions, referring to FIGS. 2-4.

System 100 provides near-instantaneous source coding that isdictionary-based and learned in advance from sample training data, sothat encoding and decoding may happen concurrently with datatransmission. This results in computational latency that is near zerobut the data size reduction is comparable to classical compression. Forexample, if N bits are to be transmitted from sender to receiver, thecompression ratio of classical compression is C the ratio between thedeflation factor of system 100 and that of multi-pass source coding isp, the classical compression encoding rate is R_(C) bit/s and thedecoding rate is R_(D) bit/s, and the transmission speed is S bit/s, thecompress-send-decompress time will be

$T_{old} = {\frac{N}{R_{C}} + \frac{N}{CS} + \frac{N}{{CR}_{D}}}$

while the transmit-while-coding time for system 100 willbe (assuming that encoding and decoding happen at least as quickly asnetwork latency):

$T_{new} = {\frac{N_{p}}{CS}\mspace{14mu} {so}}$

that the total data transit time improvement factor is

$\frac{T_{old}}{T_{new}} = \frac{\frac{CS}{R_{C}} + 1 + \frac{S}{R_{D}}}{p}$

which presents a savings whenever

${\frac{CS}{R_{C}} + \frac{S}{R_{D}}} > {p - 1.}$

This is a reasonable scenario given that typical values in real-worldpractice are C=0.32, R_(C)=1.1·10¹², R_(D)=4.2·10¹², S=10¹¹, giving

${{\frac{CS}{R_{C}} + \frac{S}{R_{D}}} = {0.053.\ldots}}\;,$

such that system 100 will outperform the total transit time of the bestcompression technology available as long as its deflation factor is nomore than 5% worse than compression. Such customized dictionary-basedencoding will also sometimes exceed the deflation ratio of classicalcompression, particularly when network speeds increase beyond 100 Gb/s.

The delay between data creation and its readiness for use at a receivingend will be equal to only the source word length t (typically 5-15bytes), divided by the deflation factor C/p and the network speed S,i.e.

${delay}_{invention} = \frac{tp}{CS}$

since encoding and decoding occur concurrently with data transmission.On the other hand, the latency associated with classical compression is

${delay}_{{prior}\; {art}} = {\frac{N}{R_{C}} + \frac{N}{CS} + \frac{N}{{CR}_{D}}}$

where N is the packet/file size. Even with the generous values chosenabove as well as N=512K, t=10, and p=1.05, this results in delayinvention≈3.3·10⁻¹⁰ while delay_(priorart)≈1.3·10⁻⁷, a more than400-fold reduction in latency.

A key factor in the efficiency of Huffman coding used by system 100 isthat key-value pairs be chosen carefully to minimize expected codinglength, so that the average deflation/compression ratio is minimized. Itis possible to achieve the best possible expected code length among allinstantaneous codes using Huffman codes if one has access to the exactprobability distribution of source words of a given desired length fromthe random variable generating them. In practice this is impossible, asdata is received in a wide variety of formats and the random processesunderlying the source data are a mixture of human input, unpredictable(though in principle, deterministic) physical events, and noise. System100 addresses this by restriction of data types and density estimation;training data is provided that is representative of the type of dataanticipated in “real-world” use of system 100, which is then used tomodel the distribution of binary strings in the data in order to build aHuffman code word library 100.

FIG. 2 is a diagram showing a more detailed architecture for acustomized library generator 200. When an incoming training data set 201is received, it may be analyzed using a frequency creator 202 to analyzefor word frequency (that is, the frequency with which a given wordoccurs in the training data set). Word frequency may be analyzed byscanning all substrings of bits and directly calculating the frequencyof each substring by iterating over the data set to produce anoccurrence frequency, which may then be used to estimate the rate ofword occurrence in non-training data. A first Huffman binary tree iscreated based on the frequency of occurrences of each word in the firstdataset, and a Huffman codeword is assigned to each observed word in thefirst dataset according to the first Huffman binary tree. Machinelearning may be utilized to improve results by processing a number oftraining data sets and using the results of each training set to refinethe frequency estimations for non-training data, so that the estimationyield better results when used with real-world data (rather than, forexample, being only based on a single training data set that may not bevery similar to a received non-training data set). A second Huffman treecreator 203 may be utilized to identify words that do not match anyexisting entries in a word library 101 and pass them to a hybridencoder/decoder 204, that then calculates a binary Huffman codeword forthe mismatched word and adds the codeword and original data to the wordlibrary 101 as a new key-value pair. In this manner, customized librarygenerator 200 may be used both to establish an initial word library 101from a first training set, as well as expand the word library 101 usingadditional training data to improve operation.

FIG. 3 is a diagram showing a more detailed architecture for a libraryoptimizer 300. A pruner 301 may be used to load a word library 101 andreduce its size for efficient operation, for example by sorting the wordlibrary 101 based on the known occurrence probability of each key-valuepair and removing low-probability key-value pairs based on a loadedthreshold parameter. This prunes low-value data from the word library totrim the size, eliminating large quantities of very-low-frequencykey-value pairs such as single-occurrence words that are unlikely to beencountered again in a data set. Pruning eliminates the least-probableentries from word library 101 up to a given threshold, which will have anegligible impact on the deflation factor since the removed entries areonly the least-common ones, while the impact on word library size willbe larger because samples drawn from asymptotically normal distributions(such as the log-probabilities of words generated by a probabilisticfinite state machine, a model well-suited to a wide variety ofreal-world data) which occur in tails of the distribution aredisproportionately large in counting measure. A delta encoder 302 may beutilized to apply delta encoding to a plurality of words to store anapproximate codeword as a value in the word library, for which each ofthe plurality of source words is a valid corresponding key. This may beused to reduce library size by replacing numerous key-value pairs with asingle entry for the approximate codeword and then represent actualcodewords using the approximate codeword plus a delta value representingthe difference between the approximate codeword and the actual codeword.Approximate coding is optimized for low-weight sources such as Golombcoding, run-length coding, and similar techniques. The approximatesource words may be chosen by locality-sensitive hashing, so as toapproximate Hamming distance without incurring the intractability ofnearest-neighbor-search in Hamming space. A parametric optimizer 303 mayload configuration parameters for operation to optimize the use of theword library 101 during operation. Best-practiceparameter/hyperparameter optimization strategies such as stochasticgradient descent, quasi-random grid search, and evolutionary search maybe used to make optimal choices for all interdependent settings playinga role in the functionality of system 100. In cases where losslesscompression is not required, the delta value may be discarded at theexpense of introducing some limited errors into any decoded(reconstructed) data.

FIG. 4 is a diagram showing a more detailed architecture for atransmission encoder/decoder 400. According to various arrangements,transmission encoder/decoder 400 may be used to deconstruct data forstorage or transmission, or to reconstruct data that has been received,using a word library 101. A library comparator 401 may be used toreceive data comprising words or codewords, and compare against a wordlibrary 101 by dividing the incoming stream into substrings of length tand using a fast hash to check word library 101 for each substring. If asubstring is found in word library 101, the corresponding key/value(that is, the corresponding source word or codeword, according towhether the substring used in comparison was itself a word or codeword)is returned and appended to an output stream. If a given substring isnot found in word library 101, a mismatch handler 402 and hybridencoder/decoder 403 may be used to handle the mismatch similarly tooperation during the construction or expansion of word library 101. Amismatch handler 402 may be utilized to identify words that do not matchany existing entries in a word library 101 and pass them to a hybridencoder/decoder 403, that then calculates a binary Huffman codeword forthe mismatched word and adds the codeword and original data to the wordlibrary 101 as a new key-value pair. The newly-produced codeword maythen be appended to the output stream. In arrangements where a mismatchindicator is included in a received data stream, this may be used topreemptively identify a substring that is not in word library 101 (forexample, if it was identified as a mismatch on the transmission end),and handled accordingly without the need for a library lookup.

Description of Method Aspects

FIG. 5 is a method diagram illustrating key system functionalityutilizing an encoder and decoder pair, according to a preferredembodiment. In a first step 501, at least one incoming data set may bereceived at a customized library generator 200 that then 502 processesdata to produce a customized word library 101 comprising key-value pairsof data words (each comprising a string of bits) and their correspondingcalculated binary Huffman codewords. A subsequent dataset may bereceived, and compared to the word library 503 to determine the propercodewords to use in order to encode the dataset. Words in the datasetare checked against the word library and appropriate encodings areappended to a data stream 504. If a word is mismatched within the wordlibrary and the dataset, meaning that it is present in the dataset butnot the word library, then a mismatched code is appended, followed bythe unencoded original word. If a word has a match within the wordlibrary, then the appropriate codeword in the word library is appendedto the data stream. Such a data stream may then be stored or transmitted505 to a destination as desired. For the purposes of decoding, analready-encoded data stream may be received and compared 506, andun-encoded words may be appended to a new data stream 507 depending onword matches found between the encoded data stream and the word librarythat is present. A matching codeword that is found in a word library isreplaced with the matching word and appended to a data stream, and amismatch code found in a data stream is deleted and the followingunencoded word is re-appended to a new data stream, the inverse of theprocess of encoding described earlier. Such a data stream may then bestored or transmitted 508 as desired.

FIG. 6 is a method diagram illustrating possible use of a hybridencoder/decoder to improve the compression ratio, according to apreferred aspect. A second Huffman binary tree may be created 601,having a shorter maximum length of codewords than a first Huffman binarytree 502, allowing a word library to be filled with every combination ofcodeword possible in this shorter Huffman binary tree 602. A wordlibrary may be filled with these Huffman codewords and words from adataset 602, such that a hybrid encoder/decoder 204, 403 may receive anymismatched words from a dataset for which encoding has been attemptedwith a first Huffman binary tree 603, 504 and parse previouslymismatched words into new partial codewords (that is, codewords that areeach a substring of an original mismatched codeword) using the secondHuffman binary tree 604. In this way, an incomplete word library may besupplemented by a second word library. New codewords attained in thisway may then be returned to a transmission encoder 605, 400. In theevent that an encoded dataset is received for decoding, and there is amismatch code indicating that additional coding is needed, a mismatchcode may be removed and the unencoded word used to generate a newcodeword as before 606, so that a transmission encoder 400 may have theword and newly generated codeword added to its word library 607, toprevent further mismatching and errors in encoding and decoding.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 7, there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (Wi-Fi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity AN hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 7 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 8, there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to system20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, memristor, orelectrical storage device for storage of data in digital form (such asthose described above, referring to FIG. 7). Examples of storage devices26 include flash memory, magnetic hard drive, CD-ROM, and/or the like.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 9, there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 8. In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as Wi-Fi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 may be used or referred to by one or more aspects. Itshould be understood by one having ordinary skill in the art thatdatabases 34 may be arranged in a wide variety of architectures andusing a wide variety of data access and manipulation means. For example,in various aspects one or more databases 34 may comprise a relationaldatabase system using a structured query language (SQL), while othersmay comprise an alternative data storage technology such as thosereferred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™,GOOGLE BIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 10 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to keyboard 49, pointing device 50,hard disk 52, and real-time clock 51. NIC 53 connects to network 54,which may be the Internet or a local network, which local network may ormay not have connections to the Internet. Also shown as part of system40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

In some embodiments, the system will comprise a pretrained (i.e.,dictionary-based) Huffman coder. Huffman coding is a source codingtechnique which is instantaneously decodable (i.e., source codewords areencoded one-by-one and independently of each other) and asymptoticallyoptimal (i.e., as encoded word length grows, the rate of thecode—corresponding to the deflation/compression ratio—approaches theentropy rate of the encoded random variable). It is thereforewell-suited to data which must be encoded for immediate transmission. Abinary Huffman codebook is a library of key-value pairs, where the keysare source words (binary strings observable in source data) and valuesare the binary codewords to which source words are translated duringencoding. Upon decoding the codebook is used in reverse: codewords areobserved in the encoded stream, and then translated back to the keyscorresponding to these values. It is possible to determine theboundaries between codewords for the purposes of this look-up becauseHuffman codes are by construction prefix-free and thereforeself-punctuating, i.e., a codeword being observed one bit at a time willnot match a value in the library until the entire word is revealed, andwhen it does so, the word will match a unique value. In an aspect ofsome embodiments, a prefix library will be built in advance, enablingthe system to conduct binary searches to find matches as a fasteralternative to bit by bit searching.

It is important that the key-value pairs be chosen carefully to minimizeexpected encoding length, i.e., the average deflation/compression ratiois minimized. It is possible to achieve the best possible expected codelength among all instantaneous codes using Huffman codes if one hasaccess to the exact probability distribution of source words of desiredlength from the random variable generating them. In practice, this isimpossible, as data comes in a virtual infinitude of formats and therandom processes underlying the source data are an inscrutable mix ofhuman input, unpredictable (but in principle deterministic) physicalevents, and noise. Therefore, in some embodiments, this problem isaddressed via a two-pronged approach: restriction of data type anddensity estimation. Users will provide a corpus of training files thatis representative of the type of data they intend to transmit. Thesystem will then model the distribution of (binary) strings in the datain order to build a Huffman code.

In some embodiments, probability distribution of source words for avariety of lengths t may be estimated using empirical word frequencieslearned from the training data. While empirical frequencies are, in manysenses, the optimal choice of estimator for probabilities, the actualobserved word distribution has unavoidably smaller support than the trueprobability distribution function. That is, there will always be wordswhich are unobserved during training but which nonetheless need to beencoded at run-time. This problem, which is defined herein as “mismatch”(since such words will not match to library keys) may be handled firstby reserving a special mismatch indicator codeword followed by aplaintext copy of the source word. Decoding will simply strip off themismatch codeword and put the next t bits into the decoded data stream.In other embodiments, a second approach, called “hybrid coding”, may beemployed to handle mismatches. In hybrid coding, a second Huffman codefor much shorter codewords (perhaps just a byte or two) will beconstructed that has an encoding of every possible source word, thusensuring that all strings can be encoded. Larger values of t give thebest ratio for the primary coding, so this hybrid coding issupplementary to ensure all source words can be encoded while maximizingdeflation. In an aspect of some embodiments, the mismatch code may becoded into the Huffman codebook, thus guaranteeing that the Huffmancoding is self-punctuating (i.e., pre-fix free), even with mismatches,and that decoding is unambiguous.

In some embodiments, the mismatch frequency may be calculated using oneor more of a variety of methods, such as using an exponentially weightedmoving average with varying weight ratios, or alternately, by estimatingthe probability q, building a code, computing an empirical new value forq and repeating.

In some embodiments, pruning may be used to reduce the size of thekey-value pair library. In pruning, the fraction of least-probablelibrary entries whose probabilities add up to some threshold are removedfrom the library. Pruning would be expected to have only a small impacton the deflation factor, because the removed words are the mostinfrequently observed ones, but the fraction of words removed will belarge because samples drawn from asymptotically normal distributions(such as the log-probabilities of words generated by a probabilisticfinite state machine, a model well-suited to a wide variety of realworld data) which occur in tails of the distribution aredisproportionately large in counting measure. This saves memory byreducing library size without impacting performance significantly.

In some embodiments, delta-encoding may be used, wherein two streams areencoded, transmitted, and decoded: a primary stream in which approximatesource words are encoded using Huffman coding, and a secondary stream inwhich the delta between true source words and the approximate sourcewords is encoded using an encoding optimized for low-weight sources suchas Golomb coding, run-length encoding, and similar. The approximatesource words may be chosen by locality sensitive hashing so as toapproximate Hamming distance without incurring the intractability ofnearest neighbor search in Hamming space. During operation, substringsof bits may be treated as unsigned binary integers. For appropriatechoices of parameters, two strings s and s′ may be expected to have thesame MinHash value F(s)=F(s′) with high probability, if and only if theyare close in Hamming distance (that is, they differ only in a few bits).This value may then be used instead of s according to the algorithmsdescribed above for computing empirical frequencies, and then a Huffmancode for all MinHash values F(s). This Huffman code is stored in alibrary along with a dictionary of key-value pairs (s,F(s)) forrepresentative strings s. The library, dictionary, and a description ofthe MinHash function F may then be shared between sender and receiverbefore transmission. When a source word s is to be encoded, the systemcomputes F(s) and looks this value up in the library to obtain acorresponding codeword. This codeword y is sent to the primarytransmission stream, and the system looks up win the dictionary toobtain a representative codeword s′. Then the delta word is computedusing a binary XOR function δ=s XOR s′. Because a delta word is expectedto be small (that is, comprise only a few bits), a code optimized forencoding low-weight codewords may be used to send a transmissionparallel to the transmission of y. Various algorithms may be used forthis including, but not limited to, run-length encoding or Golombcoding. An exemplary algorithm based on co-lexicographic ordering isnaturally suited to this task and very fast in use. First, the Hammingweight w=/δ/ is computed, and then the function colex(δ), equal to theindex of the subset of bits at which a “1” occurs in δ among the C(t,w)(the binomial coefficient for t and w) subsets of [t] of weight w,listed in colexicographic order. This integer between 0 and C(t,w)−1 isthen written in binary with exactly u=└_log₂C(t,w)−1┘+1 bits, prependedwith the unary representation of w followed by a 0 bit, and transmittedon the delta transmission stream. To decode the transmission, thereceiver matches a codeword in the transposed library, and then looks upits corresponding MinHash value. This value is then looked up in thedictionary to find a representative codeword s′. At the same time, thedelta stream is examined for its first 0 bit since the last delta wordwas recorded; the number of/s before it is recorded as w, and the next ubits are read and decoded by inverting the function colex(⋅), which canalso be done very quickly with standard combinatorial enumerationtechniques. The resulting delta word δ, having been obtained by thisprocess, is then used to compute s=s′ XOR δ, which is an exactreconstruction of the original source word s.

In some embodiments, parameter optimization may be used, whereinbest-practice parameter/hyperparameter optimization strategies such asstochastic gradient descent, quasi-random grid search, and evolutionarysearch are used to make the optimal choices for parameters of thesystem. Such parameters may include, but are not limited to, the lengtht of encoded source words, delta-encoding parameters, pruningaggressiveness, size of secondary Huffman code source words, and whetherdelta-encoding or hybrid codes are used or not (and which encodings areused for the delta stream if so).

It should be noted that, although the techniques described above aredirected toward lossless algorithms, they could be combined seriallywith lossy compression algorithms in appropriate applications.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for high-speed transfer of small datasets, comprising: a customized library generator comprising at least aplurality of programming instructions stored in the memory of, andoperating on at least one processor of, a computing device, wherein theplurality of programming instructions, when operating on the at leastone processor, cause the computing device to: receive a first datasetcomprising a plurality of words, each word comprising a string of bits,wherein the first dataset is believed to be representative of subsequentdatasets; count the plurality of words to produce an occurrencefrequency for each word; create a first Huffman binary tree based on thefrequency of occurrences of each word in the first dataset; assign aHuffman codeword to each observed word in the first dataset according tothe first Huffman binary tree; construct a word library, wherein theword library stores the codewords and their corresponding words askey-value pairs in the library of key-value pairs; create a secondHuffman binary tree with a maximum codeword length shorter than themaximum codeword length in the first Huffman binary tree, and containingall combinations of such codewords to that shorter maximum length;assign a word to each Huffman codeword in the second Huffman binarytree; and add each word and its corresponding codeword, to the wordlibrary as key-value pairs in the library of key-value pairs; atransmission encoder comprising at least a plurality of programminginstructions stored in the memory of, and operating on at least oneprocessor of, a computing device, wherein the plurality of programminginstructions, when operating on the at least one processor, cause thecomputing device to: receive one or more subsequent datasets, eachcomprising a plurality of words, each word comprising a string of bits;compare each word in the subsequent dataset or datasets against the wordlibrary; if a word is not a mismatch, append the word's codeword to atransmission data stream; if a word is a mismatch, append a mismatchcode to the transmission data stream followed by the unencoded word; andtransmit or store the transmission data stream; a transmission decoder,comprising at least a plurality of programming instructions stored inthe memory of, and operating on at least one processor of, a computingdevice, wherein the plurality of programming instructions, whenoperating on the at least one processor, cause the computing device to:receive one or more datasets, each comprising a plurality of codewords,each codeword comprising a string of bits; compare each codeword in thedataset or datasets against the word library; if a codeword is not amismatch, append the codeword's word to a transmission data stream; if acodeword is a mismatch codeword, discard the mismatch code and appendthe following word to the transmission data stream; and transmit orstore the transmission data stream; a hybrid encoder comprising at leasta plurality of programming instructions stored in the memory of, andoperating on at least one processor of, a computing device, wherein theplurality of programming instructions, when operating on the at leastone processor, cause the computing device to: receive mismatched wordsfrom the transmission encoder; parse the mismatched words into partialwords that correspond to a codeword in the second Huffman tree; andreturn the codeword for each partial word to the transmission encoder;and a hybrid decoder comprising at least a plurality of programminginstructions stored in the memory of, and operating on at least oneprocessor of, a computing device, wherein the plurality of programminginstructions, when operating on the at least one processor, cause thecomputing device to: receive mismatched codewords from the transmissiondecoder; compare each codeword against the word library; and return theword associated with the mismatched codeword to the transmissionencoder.
 2. The system of claim 1, wherein the library size is reducedby sorting the word library based on the occurrence probability of eachkey-value pair and removing low-probability key-value pairs.
 3. Thesystem of claim 1, wherein delta encoding is applied to a plurality ofwords to store an approximate codeword as a value in the word library,for which each of the plurality of source words is a valid correspondingkey.
 4. The system of claim 3, wherein the exclusive or (XOR) correctionresulting from the delta encoding is discarded, resulting in a faster,but lossy compression algorithm.
 5. The system of claim 1, whereinparameters of the system are optimized according to the datasets beingused.
 6. A method for high-speed transmission of small data sets using aword library, comprising the steps of: receiving a first datasetcomprising a plurality of words, each word comprising a string of bits,wherein the first dataset is believed to be representative of subsequentdatasets; counting the plurality of words to produce an occurrencefrequency for each word; creating a first Huffman binary tree based onthe frequency of occurrences of each word in the first dataset;assigning a Huffman codeword to each observed word in the first datasetaccording to the first Huffman binary tree; constructing a word library,wherein the word library stores the codewords and their correspondingwords as key-value pairs in the library of key-value pairs; creating asecond Huffman binary tree with a maximum codeword length shorter thanthe maximum codeword length in the first Huffman binary tree, andcontaining all combinations of such codewords to that shorter maximumlength; assigning a word to each Huffman codeword in the second Huffmanbinary tree; adding each word and its corresponding codeword, to theword library as key-value pairs in the library of key-value pairs;receiving, at a transmission encoder one or more subsequent datasets,each comprising a plurality of words, each word comprising a string ofbits; comparing each word in the subsequent dataset or datasets againstthe word library; if a word is not a mismatch, appending the word'scodeword to a transmission data stream; if a word is a mismatch,appending a mismatch code to the transmission data stream followed bythe unencoded word; transmitting or storing the transmission datastream; receiving, at a transmission decoder, one or more datasets, eachcomprising a plurality of codewords, each codeword comprising a stringof bits; comparing each codeword in the dataset or datasets against theword library; if a codeword is not a mismatch, append the codeword'sword to a transmission data stream; if a codeword is a mismatchcodeword, discard the mismatch code and append the following word to thetransmission data stream; transmitting or storing the transmission datastream; receiving, at a hybrid encoder, mismatched words from thetransmission encoder; parsing the mismatched words into partial wordsthat correspond to a codeword in the second Huffman tree; returning thecodeword for each partial word to the transmission encoder; receiving,at a hybrid decoder, mismatched codewords from the transmission decoder;comparing each codeword against the word library; and returning the wordassociated with the mismatched codeword to the transmission encoder. 7.The method of claim 6, wherein the library size is reduced by sortingthe word library based on the occurrence probability of each key-valuepair and removing low-probability key-value pairs.
 8. The method ofclaim 6, wherein delta encoding is applied to a plurality of words tostore an approximate codeword as a value in the word library, for whicheach of the plurality of source words is a valid corresponding key. 9.The system of claim 8, wherein the exclusive or (XOR) correctionresulting from the delta encoding is discarded, resulting in a faster,but lossy compression algorithm.
 10. The method of claim 6, whereinparameters of the system are optimized according to the datasets beingused.